Machine learning for real-time prediction of complications induced by flexible uretero-renoscopy with laser lithotripsy - Research

Aug 3, 2026

Surgical outcomes depend on patient-specific conditions and procedural context, making reliable prediction of intra- and postoperative complications critical for risk reduction. This study proposes an intelligent machine-learning system to predict complications associated with flexible ureterorenoscopy combined with laser lithotripsy for kidney-stone treatment. The approach reports strong predictive performance, supported by SHAP-based feature interpretation to identify influential variables. Results combine high accuracy with model explainability to support safer clinical decision-making.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Introduction

Risks and patient factors in surgery

Complications of uretero-renoscopy and classifications

Motivation for machine learning decision support

Machine learning for real-time prediction of complications induced by flexible uretero-renoscopy with laser lithotripsy Chafik Baidada1, Hamid Hrimech2, Mustapha Aatila1, Mohamed Lachgar1, Younes Ommane3 1Information Technology Laboratory, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, Morocco 2Analysis and Modeling of Systems and Decision Support Laboratory, National School of Applied Sciences of Berrechid, Hassan 1er University of Settat, Berrechid, Morocco 3Institute of Biological Sciences (ISSB), UM6P-Faculty of Medical Sciences (UM6P-FMS), Mohammed VI Polytechnic University, Ben-Guerir, Morocco Article Info ABSTRACT | Article history:
Received Jul 31, 2023 Revised Sep 9, 2023 Accepted Sep 12, 2023 | It is not always easy to predict the outcome of a surgery. Peculiarly, when talking about the risks associated to a given intervention or the possible complications that it may bring about. Thus, predicting those potential complications that may arise during or after a surgery will help minimize risks and prevent failures to the greatest extent possible. Therefore, the objectif of this article is to propose an intelligent system based on machine learning, allowing predicting the complications related to a flexible ureterorenoscopy with laser lithotripsy for the treatment of kidney stones. The proposed method achieved accuracy with 100% for training and, 94.33% for testing in hard voting, 100% for testing and 95.38% for training in soft voting, with only ten optimal features. Additionally, we were able to evaluted the machine learning model by examining the most significant features using the Shaply additive explanations (SHAP) feature importance plot, dependency plot, summary plot, and partial dependency plots.
This is an open access article under the CC BY-SA license.
| | --- | --- | | Keywords:
Classification
Flexible uretero-renoscopy Machine learning Prediction
Study | | Corresponding Author: Chafik Baidada Information Technology Laboratory, National School of Applied Sciences, Chouaib Doukkali University El Jadida, Morocco Email: [baidada.c@ucd.ac.ma](baidada.c@ucd.ac.ma) 1. INTRODUCTION A surgical intervention has a number of risks, some of which are more likely than others depending on the circumstances. Surgical procedures such as cardiothoracic or pulmonary surgery, hepatobiliary surgery, abdominal surgery, prostate removal, and major osseous and articular surgery all have higher risks. Further, depending on their health, certain patients are at higher risk of complications: malnutrition, diabetes, obesity, and antecedents of cardiac insufficiency are all factors that increase the surgical risk. Like all operations, kidney stone surgery by uretero-renoscopy (URS) presents an increasing rate of infectious complications. These URS related complications may increase the risk of mortality and morbidity especially in high-risk patients. Prophylactic antibiotic use, stent and operation dwell time restriction, early detection and treatment of urinary tract infections (UTIs) and urosepsis, careful planning in patients presenting with a load of substantial calculus and numerous comorbidities are some examples of these complications. With improving technology, flexible minimally invasive URS with laser lithotripsy (FURSLL) has recently been introduced as a new technic to improve the outcome of ureterorenal stone surgery. Even for individuals with proximal kidney and ureter stones smaller than 20 mm, FURSLL has become an attractive option [1], [2] . For this kind of stones, stone-free FURSLL rates can be as high as 94.1% with an overall 28% risk of preoperative complications [3], [4] . Although flexible URS lithotripsy is a less intrusive procedure, there is a possibility that complications could emerge [5]–[7] . The complications caused by URS have been divided into two categories: minor and major. complications are considered minors if they can b

FAQs

What problem does this study address?

The study addresses the difficulty of predicting surgical outcomes and the complications that may occur during or after flexible ureterorenoscopy with laser lithotripsy for kidney stones.

How does the proposed method support prediction?

It uses a machine-learning–based intelligent decision support system that predicts complications for patients undergoing the procedure, using a limited set of optimal features and voting strategies.

How are model results interpreted in this research?

The study evaluates feature importance and interpretability using SHAP (Shapley Additive Explanations), including feature importance, dependency, summary, and partial dependency plots.

Aug 3, 2026

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